Application of Aerial Digital Photography for Macrophyte Cover and Composition Survey in Small Rural Streams

Abstract

Macrophytes are a key biological component in many fluvial ecosystems. In altered streams, they can become highly dominant features, covering extensive parts of the channel with cascading effects on stream conditions and biological composition. The aim of this study is to examine the possibility of using information collected by aerial digital photography‐spectral analysis (ADP‐SA) as an alternative, cost‐effective methodology to the commonly used spectral procedure at a large (section) scale, and to the ground level visual survey (gridded frame) at a smaller (organism) scale. Cladophora glomerata and Nasturtium officinale, were selected as the targeted macrophyte species and classified at the same time (species relative cover) by using the three methodologies. Our findings show that the ADP‐SA methodology was able to detect species and relative cover in similar accuracy (≤10% differences) at the two spatial scales. ADP‐SA had a better spatial resolution than both the hyperspectral and the visual methodology (4 cm vs. 1 m and 10 cm, respectively) and was capable to differentiate submerged from emergent plants. However, on a smaller scale, ground level work is suitable and essential for detecting rarer species and is not hindered by weather, canopy cover or multi‐layered plant composition. ADP‐SA can therefore add a cost‐effective and nonsubjective practice to the existing tool kit of macrophyte surveys, particularly in small streams, which require high spatial resolution. Copyright © 2013 John Wiley & Sons, Ltd.

Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle

“…In this case, the target spectrum (Cladophora), image background (diatoms and bare substrate), and optically deep water are all different. Cladophora reflect more green and red than either of the other two classes (see magnitude of digital number for support of this statement), which is consistent with reported reflectance ratios in the literature [25,64,65]. It is important to note that the spectrum in Figure 7 will change according to lighting conditions, algal condition, or optical clarity of the river water.…”

“…platforms [25,26]. Aquatic plants can be digitally identified with spectral reflectance [24][25][26][27], therefore SAV presumably can be characterized across multiple space and time scales with remotely sensed data [28].…”

“…Aquatic plants can be digitally identified with spectral reflectance [24][25][26][27], therefore SAV presumably can be characterized across multiple space and time scales with remotely sensed data [28]. Prior work (non-SAV related) has been completed using anything from handheld poles to commercial satellites [28], unmanned aerial vehicles (UAVs) [26], even kites and balloons [29,30] to map habitat, channel geomorphology, the water surface, or bathymetric features [2] at centimeter to meter scales.…”

“…Prior work (non-SAV related) has been completed using anything from handheld poles to commercial satellites [28], unmanned aerial vehicles (UAVs) [26], even kites and balloons [29,30] to map habitat, channel geomorphology, the water surface, or bathymetric features [2] at centimeter to meter scales. The use of conventional RGB aerial photography [25,26] or multispectral cameras [31] in these applications obviously requires the river water column to be clear and not obstructed by trees or overhanging banks [2,24].…”

Quantifying the Effect of Aerial Imagery Resolution in Automated Hydromorphological River Characterisation

“…In [27], the spectral resolution was more relevant than the spatial or radiometric resolution for the automated identification of in-channel features (riffles, glides, pools and eddy drop zones). Anker et al [28] focused on the effectiveness of aerial digital photography-spectral analysis (ADP-SP) to assess macrophyte cover when compared to ground-level data and hyperspectral imagery. ADP-SP presented a better spatial resolution than ground-level and hyperspectral data (4 cm vs. 1 m and 10 cm) and enabled the differentiation between emergent and submerged vegetation.…”